[ffsm8]

All models hallucinate. The likelihood of hallucinations are however strongly influenced by the way you prompt and construct your context.

But even if a human went through the documents by hand and tried to make the analysis, they're still likely to make mistakes. That's why we usually define the scientific method as making falsifiable claims, which you then try to disprove in order to make sure they're correct.

And if you can't do that, then you're always walking on thin ice, whatever tool or methodology you choose to use for the analysis.

[belter]

> hallucinations are however strongly influenced by the way you prompt and construct your context.

Show me the research supporting this argument. So far RAG and similar approaches is what limits hallucinations.

[ffsm8]

Are you serious unaware what a RAG is and still speak with authority on the topic?

It's automatically retrieving information and adding it to the context. It's -in spirit- a convenience function so you don't have to manually provide it during the prompt. It's just a lot harder to pull off well automatically, but the fundamental practice is "just" context optimization

You're essentially saying "but that's not driving!!!!" After someone goes by in an EV, because it's ain't an ICE

[belter]

Not the same: "RAG vs. Long-context LLMs" - https://www.superannotate.com/blog/rag-vs-long-context-llms

[ffsm8]

You're literally linking to an article that confirmed what I said. Yes, a model that has RAG will be able to perform with a lot smaller context size.

That doesn't mean RAG isn't context optimization.

[belter]

Did you made any technical argument on how to reduce hallucinations? Because I fail to see one from you on this thread except: "it's the fault of your prompt""